#Report Sections:
#1. Introduction
#2. Data
#3. Methodology
#4. Results
#5. Discussion
#6. Conclusion
In Progress¶
Residential Evictions In New York City¶
Introduction¶
New York City is the largest city in the United States and arguably the financial capital of the world. Its cost of living is very high, to the point where Economist Intelligence Unit's 2020 Worldwide Cost of Living Survey puts it in the fourth place in the world. Singapore, Osaka and Hong Kong tied for the first place. It also has one of the most competitive and expensive real estate in the country. As to be expected, a large portion of the businesses and homes are leased properties. This study focuses on such residential properties and their renters. High population density, lack of space for horizontal growth and extensive vertical growth make evictions an unfortunate reality for residents of the city. I looked into residents' household income, their level of education and prevalence of certain types of crime in the neighborhood and how they relate to evictions per capita. Not unexpectedly, there appears to be a strong correlation between all these factors.
Most of the data is categorized by neighborhood, except for arrest data. I could only find arrest data by precinct. Thus the study is largely exploratory.
Data¶
Data used in this study, their purpose and their sources are discussed below:
- Evictions: The study focuses on evictions in New York City neighborhoods, which makes this data central to the work. The rest of the project is built on top of findings from the dataset on evictions. (Data Source: New York City Government through NYC Open Data; beginning of 2017 to March 13, 2020)
- Geographical Coordinates: The coordinates were necessary to plot eviction data on the map. (Data Source: OpenStreetMap Nominatim package)
- Population data for cities and neighborhoods: Some neighborhoods have larger population than others. Expressing number of evictions per capita (per thousand) helps normalize this factor. (Data Source: Wikipedia)
- Income data by cities and neighborhoods: Lower income leaves one without a safety net, increasing financial undertainties. It is one of the measures I compared with eviction rates. (Data Source: Census Bureau American Community Survey Economic Profile; 2012 to 2016)
- Education level by cities and neighborhoods: Higher educational level generally indicates better knowledge of principles of money management. Since jobs requiring higher levels of education often pay better, it has a strong correlation with income. (Data Source: Census Bureau American Community Survey Social Profile; 2012 to 2016)
- Criminal activity in cities and neighborhoods: Criminal activities in an area generate fear in its residents. The preception of lack of safety make properties less desirable which push prices down. In the long run this dynamic leads such places to lower income and poverty. I compared the distribution of higher crime areas to eviction rates as part of the study. (Data Source: NYPD Arrests Data through NYC Open Data; Beginning of 2006 to end of 2019)
Methodology¶
When we think of people getting evicted from their home we think of economically depressed areas. The features of such a place include lower income, lower levels of education and higher crime rate. The idea is such conditions set up a vicious cycle that feeds into the higher eviction rate. This is an academic exercise to verify this abstract theory by studying if such socioeconomic conditions truly coexist in neighborhoods with high rates of residential evictions.
We have quite a few possible ways of approaching this problem: some more mathematical, while others visual. In this study, my chosen mode of answering this question is largely descriptive and visual. Since we are addressing evictions, I started with the eviction data sourced from NYC Open Data. The original dataframe was grouped by zip code and only columns showing zip code, neighborhood name and count of evictions by zip code were retained. The dataframe was merged with a separate dataframes listing latitude and longitude for zip codes across the country. The location of evictions, residential as well as commercial, were plotted on a map of New York City. It's important to note that the rest of the project focuses exclusively on residential evictions.
Unfortunately I could not find latitude and longitude for several zip code and there were no reliable source of population data grouped by zip code. So I had to change direction. The new approach involved grouping data by neighborhood names (also referred to as cities in some regions). OpenStreetMap Nominatim was used to add geographical coordinates to the dataframe. Population, grouped by neighborhood, was obtained from "New York City Population by Neighborhood Tabulation Areas" dataset obtained through NYC Open Data. Missing values were filled in using data from Wikipedia. Number of evictions for each neighborhood was normalized by population and expressed as per capita (per thousand). The data was plot on a map. The data on socioeconomic factors such as median income and estimate of percentage of residents with a bachelor's degree or above were treated and mapped in a similar manner.
As mentioned previously, crime has a large effect on the perception of safety, and in turn property prices. The total number of arrests for certain types of crimes were plotted on a map at the precinct level. The types of criminal activities included are the ones NYPD describes as the 'seven major felony offenses': i) murder and non-negligent manslaughter; ii) rape; iii) felony assault; iv) robbery; v) burglary; vi) grand larceny; and, vii) grand larceny of motor vehicle.
Results¶
Figure below shows total number of evictions across the city for each of the three years between 2017 and 2019. The numbers includes evictions from both, residential as well as commercial properties. The number decreased each year. The actual values reveal a little over 17 percent drop in evictions in the 3 years. While outside the scope of this project, a possible reason behind the drop is the improving economy and decreasing unemployment.
The picture below is a screenshot of an interactive map created using the Folium package.
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